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Co‐occurrence patterns in diagnostic data
Author(s) -
Piceno Marie Ely,
RodríguezNavas Laura,
Balcázar José Luis
Publication year - 2021
Publication title -
computational intelligence
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.353
H-Index - 52
eISSN - 1467-8640
pISSN - 0824-7935
DOI - 10.1111/coin.12317
Subject(s) - computer science , graph , visualization , decomposition , theoretical computer science , power graph analysis , data mining , ecology , biology
Summary We demonstrate how graph decomposition techniques can be employed for the visualization of hierarchical co‐occurrence patterns between medical data items. Our research is based on Gaifman graphs (a mathematical concept introduced in Logic), on specific variants of this concept, and on existing graph decomposition notions, specifically, graph modules and the clan decomposition of so‐called 2‐structures. The construction of the Gaifman graphs from a dataset is based on co‐occurrence, or lack of it, of items in the dataset. We may select a discretization on the edge labels to aim at one among several Gaifman graph variants. Then, the decomposition of the graph may provide us with visual information about the data co‐occurrences, after which one can proceed to more traditional statistical analysis.